from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 53.539513 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 6.734521 |
| KNeighborsClassifier_kd_tree | 0.0 | 7.0 | 54.972622 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 52.993847 |
| KMeans_tall | 0.0 | 1.0 | 39.278736 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 17.371433 |
| KMeans_short | 0.0 | 0.0 | 19.108480 |
| daal4py_KMeans_short | 0.0 | 0.0 | 7.865974 |
| LogisticRegression | 0.0 | 1.0 | 1.530703 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 55.618301 |
| Ridge | 0.0 | 0.0 | 46.014546 |
| daal4py_Ridge | 0.0 | 0.0 | 14.818841 |
| total | 0.0 | 33.0 | 9.920891 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.142 | 0.003 | 1000000 | 1000000 | 100 | brute | -1 | 1 | NaN | NaN | 0.487 | 0.004 | 0.292 | 0.006 | See |
| 1 | KNeighborsClassifier | predict | 0.161 | 0.010 | 1000000 | 1 | 100 | brute | -1 | 1 | 1.0 | 1.0 | 0.096 | 0.003 | 1.680 | 0.120 | See |
| 2 | KNeighborsClassifier | predict | 28.035 | 0.378 | 1000000 | 1000 | 100 | brute | -1 | 1 | 1.0 | 1.0 | 1.775 | 0.024 | 15.795 | 0.303 | See |
| 3 | KNeighborsClassifier | fit | 0.136 | 0.003 | 1000000 | 1000000 | 100 | brute | -1 | 5 | NaN | NaN | 0.501 | 0.006 | 0.272 | 0.008 | See |
| 4 | KNeighborsClassifier | predict | 0.172 | 0.010 | 1000000 | 1 | 100 | brute | -1 | 5 | 1.0 | 1.0 | 0.092 | 0.001 | 1.863 | 0.106 | See |
| 5 | KNeighborsClassifier | predict | 36.185 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 5 | 1.0 | 1.0 | 1.798 | 0.028 | 20.128 | 0.311 | See |
| 6 | KNeighborsClassifier | fit | 0.132 | 0.003 | 1000000 | 1000000 | 100 | brute | -1 | 100 | NaN | NaN | 0.491 | 0.003 | 0.270 | 0.006 | See |
| 7 | KNeighborsClassifier | predict | 0.177 | 0.011 | 1000000 | 1 | 100 | brute | -1 | 100 | 1.0 | 1.0 | 0.096 | 0.001 | 1.844 | 0.116 | See |
| 8 | KNeighborsClassifier | predict | 36.040 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 100 | 1.0 | 1.0 | 1.824 | 0.024 | 19.763 | 0.264 | See |
| 9 | KNeighborsClassifier | fit | 0.133 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 1 | NaN | NaN | 0.500 | 0.008 | 0.266 | 0.007 | See |
| 10 | KNeighborsClassifier | predict | 0.181 | 0.002 | 1000000 | 1 | 100 | brute | 1 | 1 | 1.0 | 1.0 | 0.095 | 0.002 | 1.916 | 0.047 | See |
| 11 | KNeighborsClassifier | predict | 13.766 | 0.058 | 1000000 | 1000 | 100 | brute | 1 | 1 | 1.0 | 1.0 | 1.803 | 0.005 | 7.635 | 0.039 | See |
| 12 | KNeighborsClassifier | fit | 0.134 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 5 | NaN | NaN | 0.490 | 0.004 | 0.274 | 0.007 | See |
| 13 | KNeighborsClassifier | predict | 0.193 | 0.001 | 1000000 | 1 | 100 | brute | 1 | 5 | 1.0 | 1.0 | 0.096 | 0.002 | 2.001 | 0.041 | See |
| 14 | KNeighborsClassifier | predict | 22.715 | 0.132 | 1000000 | 1000 | 100 | brute | 1 | 5 | 1.0 | 1.0 | 1.775 | 0.016 | 12.799 | 0.136 | See |
| 15 | KNeighborsClassifier | fit | 0.135 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 100 | NaN | NaN | 0.495 | 0.008 | 0.272 | 0.007 | See |
| 16 | KNeighborsClassifier | predict | 0.193 | 0.001 | 1000000 | 1 | 100 | brute | 1 | 100 | 1.0 | 1.0 | 0.092 | 0.001 | 2.098 | 0.030 | See |
| 17 | KNeighborsClassifier | predict | 22.245 | 0.145 | 1000000 | 1000 | 100 | brute | 1 | 100 | 1.0 | 1.0 | 1.861 | 0.024 | 11.956 | 0.173 | See |
| 18 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 1 | NaN | NaN | 0.098 | 0.003 | 0.582 | 0.022 | See |
| 19 | KNeighborsClassifier | predict | 0.020 | 0.001 | 1000000 | 1 | 2 | brute | -1 | 1 | 1.0 | 1.0 | 0.006 | 0.000 | 3.449 | 0.264 | See |
| 20 | KNeighborsClassifier | predict | 24.909 | 0.077 | 1000000 | 1000 | 2 | brute | -1 | 1 | 1.0 | 1.0 | 0.261 | 0.005 | 95.353 | 1.685 | See |
| 21 | KNeighborsClassifier | fit | 0.059 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 5 | NaN | NaN | 0.095 | 0.003 | 0.622 | 0.022 | See |
| 22 | KNeighborsClassifier | predict | 0.029 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 5 | 1.0 | 1.0 | 0.008 | 0.003 | 3.432 | 1.096 | See |
| 23 | KNeighborsClassifier | predict | 34.802 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 5 | 1.0 | 1.0 | 0.269 | 0.007 | 129.577 | 3.360 | See |
| 24 | KNeighborsClassifier | fit | 0.063 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 100 | NaN | NaN | 0.097 | 0.001 | 0.655 | 0.012 | See |
| 25 | KNeighborsClassifier | predict | 0.031 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 100 | 1.0 | 1.0 | 0.006 | 0.000 | 5.034 | 0.415 | See |
| 26 | KNeighborsClassifier | predict | 33.190 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 100 | 1.0 | 1.0 | 0.319 | 0.008 | 104.096 | 2.731 | See |
| 27 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | brute | 1 | 1 | NaN | NaN | 0.100 | 0.003 | 0.567 | 0.020 | See |
| 28 | KNeighborsClassifier | predict | 0.017 | 0.000 | 1000000 | 1 | 2 | brute | 1 | 1 | 1.0 | 1.0 | 0.006 | 0.000 | 2.883 | 0.144 | See |
| 29 | KNeighborsClassifier | predict | 10.811 | 0.055 | 1000000 | 1000 | 2 | brute | 1 | 1 | 1.0 | 1.0 | 0.263 | 0.007 | 41.117 | 1.070 | See |
| 30 | KNeighborsClassifier | fit | 0.055 | 0.001 | 1000000 | 1000000 | 2 | brute | 1 | 5 | NaN | NaN | 0.097 | 0.001 | 0.572 | 0.010 | See |
| 31 | KNeighborsClassifier | predict | 0.024 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 5 | 1.0 | 1.0 | 0.006 | 0.000 | 4.081 | 0.171 | See |
| 32 | KNeighborsClassifier | predict | 19.622 | 0.031 | 1000000 | 1000 | 2 | brute | 1 | 5 | 1.0 | 1.0 | 0.265 | 0.004 | 74.143 | 1.196 | See |
| 33 | KNeighborsClassifier | fit | 0.068 | 0.002 | 1000000 | 1000000 | 2 | brute | 1 | 100 | NaN | NaN | 0.098 | 0.001 | 0.692 | 0.022 | See |
| 34 | KNeighborsClassifier | predict | 0.025 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 100 | 1.0 | 1.0 | 0.006 | 0.000 | 4.126 | 0.229 | See |
| 35 | KNeighborsClassifier | predict | 19.572 | 0.086 | 1000000 | 1000 | 2 | brute | 1 | 100 | 1.0 | 1.0 | 0.322 | 0.005 | 60.864 | 0.955 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 3.429 | 0.066 | 1000000 | 1000000 | 10 | kd_tree | -1 | 1 | NaN | NaN | 0.801 | 0.015 | 4.278 | 0.116 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 1 | 1.0 | 1.0 | 0.000 | 0.000 | 20.179 | 17.881 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.498 | 0.007 | 1000000 | 1000 | 10 | kd_tree | -1 | 1 | 1.0 | 1.0 | 0.130 | 0.002 | 3.834 | 0.069 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 3.600 | 0.035 | 1000000 | 1000000 | 10 | kd_tree | -1 | 5 | NaN | NaN | 0.828 | 0.008 | 4.349 | 0.059 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 5 | 1.0 | 1.0 | 0.000 | 0.000 | 13.059 | 7.443 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.966 | 0.005 | 1000000 | 1000 | 10 | kd_tree | -1 | 5 | 1.0 | 1.0 | 0.232 | 0.003 | 4.158 | 0.061 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 3.729 | 0.040 | 1000000 | 1000000 | 10 | kd_tree | -1 | 100 | NaN | NaN | 0.801 | 0.011 | 4.655 | 0.080 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 100 | 1.0 | 1.0 | 0.000 | 0.000 | 9.316 | 4.779 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 3.178 | 0.028 | 1000000 | 1000 | 10 | kd_tree | -1 | 100 | 1.0 | 1.0 | 0.708 | 0.007 | 4.490 | 0.059 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.533 | 0.077 | 1000000 | 1000000 | 10 | kd_tree | 1 | 1 | NaN | NaN | 0.827 | 0.014 | 4.272 | 0.117 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 1 | 1.0 | 1.0 | 0.000 | 0.000 | 5.411 | 4.407 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.860 | 0.005 | 1000000 | 1000 | 10 | kd_tree | 1 | 1 | 1.0 | 1.0 | 0.130 | 0.003 | 6.609 | 0.151 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.589 | 0.048 | 1000000 | 1000000 | 10 | kd_tree | 1 | 5 | NaN | NaN | 0.801 | 0.010 | 4.481 | 0.082 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 5 | 1.0 | 1.0 | 0.000 | 0.000 | 4.849 | 3.605 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1.615 | 0.011 | 1000000 | 1000 | 10 | kd_tree | 1 | 5 | 1.0 | 1.0 | 0.234 | 0.002 | 6.892 | 0.071 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.627 | 0.051 | 1000000 | 1000000 | 10 | kd_tree | 1 | 100 | NaN | NaN | 0.838 | 0.008 | 4.330 | 0.073 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 100 | 1.0 | 1.0 | 0.000 | 0.000 | 5.572 | 3.093 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 5.421 | 0.015 | 1000000 | 1000 | 10 | kd_tree | 1 | 100 | 1.0 | 1.0 | 0.694 | 0.003 | 7.809 | 0.041 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 2.122 | 0.094 | 1000000 | 1000000 | 2 | kd_tree | -1 | 1 | NaN | NaN | 0.500 | 0.007 | 4.247 | 0.197 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 1 | 1.0 | 1.0 | 0.000 | 0.000 | 19.122 | 15.959 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.024 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 1 | 1.0 | 1.0 | 0.001 | 0.000 | 31.255 | 12.925 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 2.114 | 0.049 | 1000000 | 1000000 | 2 | kd_tree | -1 | 5 | NaN | NaN | 0.514 | 0.010 | 4.117 | 0.123 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.001 | 1000000 | 1 | 2 | kd_tree | -1 | 5 | 1.0 | 1.0 | 0.000 | 0.000 | 21.497 | 18.696 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.027 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 5 | 1.0 | 1.0 | 0.001 | 0.000 | 23.178 | 7.191 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 2.149 | 0.087 | 1000000 | 1000000 | 2 | kd_tree | -1 | 100 | NaN | NaN | 0.506 | 0.021 | 4.248 | 0.244 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 100 | 1.0 | 1.0 | 0.000 | 0.000 | 14.576 | 11.833 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.049 | 0.004 | 1000000 | 1000 | 2 | kd_tree | -1 | 100 | 1.0 | 1.0 | 0.007 | 0.001 | 6.532 | 0.730 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 2.030 | 0.090 | 1000000 | 1000000 | 2 | kd_tree | 1 | 1 | NaN | NaN | 0.514 | 0.017 | 3.950 | 0.219 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 1 | 1.0 | 1.0 | 0.000 | 0.000 | 5.703 | 5.606 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.022 | 0.000 | 1000000 | 1000 | 2 | kd_tree | 1 | 1 | 1.0 | 1.0 | 0.001 | 0.000 | 31.017 | 13.898 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1.963 | 0.151 | 1000000 | 1000000 | 2 | kd_tree | 1 | 5 | NaN | NaN | 0.490 | 0.016 | 4.004 | 0.336 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 5 | 1.0 | 1.0 | 0.000 | 0.000 | 5.127 | 4.979 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.024 | 0.000 | 1000000 | 1000 | 2 | kd_tree | 1 | 5 | 1.0 | 1.0 | 0.002 | 0.002 | 12.868 | 15.166 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1.937 | 0.100 | 1000000 | 1000000 | 2 | kd_tree | 1 | 100 | NaN | NaN | 0.510 | 0.014 | 3.801 | 0.221 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 100 | 1.0 | 1.0 | 0.000 | 0.000 | 4.466 | 3.855 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.055 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 100 | 1.0 | 1.0 | 0.010 | 0.006 | 5.334 | 3.031 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.600 | 0.050 | 1000000 | 1000000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 0.392 | 0.015 | 1.531 | 0.140 | See |
| 1 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.768 | 1.603 | See |
| 2 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 2.623 | 2.346 | See |
| 3 | KMeans_tall | fit | 0.503 | 0.006 | 1000000 | 1000000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 0.358 | 0.015 | 1.403 | 0.061 | See |
| 4 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | random | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.924 | 1.998 | See |
| 5 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | random | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.818 | 1.662 | See |
| 6 | KMeans_tall | fit | 6.375 | 0.105 | 1000000 | 1000000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 3.147 | 0.048 | 2.026 | 0.045 | See |
| 7 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.604 | 1.315 | See |
| 8 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 2.052 | 1.365 | See |
| 9 | KMeans_tall | fit | 5.746 | 0.070 | 1000000 | 1000000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 2.988 | 0.047 | 1.923 | 0.038 | See |
| 10 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | random | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.752 | 1.728 | See |
| 11 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | random | 30 | 3 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 2.023 | 1.317 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.282 | 0.011 | 10000 | 10000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 0.090 | 0.003 | 3.121 | 0.160 | See |
| 1 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.865 | 1.539 | See |
| 2 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.001 | 0.000 | 1.136 | 0.345 | See |
| 3 | KMeans_short | fit | 0.110 | 0.002 | 10000 | 10000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 0.042 | 0.002 | 2.629 | 0.107 | See |
| 4 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | random | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.785 | 1.682 | See |
| 5 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | random | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.001 | 0.000 | 1.081 | 0.318 | See |
| 6 | KMeans_short | fit | 0.904 | 0.041 | 10000 | 10000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 19.0 | NaN | 20.0 | NaN | 0.335 | 0.011 | 2.703 | 0.150 | See |
| 7 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.610 | 1.016 | See |
| 8 | KMeans_short | predict | 0.007 | 0.003 | 10000 | 1000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.001 | 0.000 | 6.232 | 2.530 | See |
| 9 | KMeans_short | fit | 0.261 | 0.033 | 10000 | 10000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30.0 | NaN | 30.0 | NaN | 0.184 | 0.019 | 1.416 | 0.233 | See |
| 10 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | random | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.000 | 0.000 | 1.680 | 1.161 | See |
| 11 | KMeans_short | predict | 0.008 | 0.001 | 10000 | 1000 | 100 | full | random | 30 | 300 | 1 | 0.0 | NaN | 1.0 | NaN | 1.0 | 0.001 | 0.000 | 6.387 | 1.309 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 11.151 | 0.026 | 1000000 | 1000000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 11.414 | 0.034 | 0.977 | 0.004 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | NaN | 0.000 | 0.000 | 0.379 | 0.446 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | NaN | 0.000 | 0.000 | 0.788 | 0.496 | See |
| 3 | LogisticRegression | fit | 0.794 | 0.017 | 1000 | 1000 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [27] | 0.793 | 0.025 | 1.002 | 0.038 | See |
| 4 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | NaN | 0.002 | 0.002 | 0.059 | 0.085 | See |
| 5 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | NaN | 0.003 | 0.000 | 0.546 | 0.138 | See |
Shared hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | n_iter | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1.723 | 0.045 | 100000 | 100000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 0.916 | 0.032 | 1.882 | 0.083 | See |
| 1 | Ridge | predict | 0.000 | 0.000 | 100000 | 1 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 0.000 | 0.000 | 0.668 | 0.827 | See |
| 2 | Ridge | predict | 0.001 | 0.000 | 100000 | 1000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 0.001 | 0.000 | 0.810 | 0.509 | See |
| 3 | Ridge | fit | 1.134 | 0.016 | 1000000 | 1000000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 0.229 | 0.003 | 4.945 | 0.094 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 0.000 | 0.000 | 0.706 | 0.879 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 0.000 | 0.000 | 0.535 | 0.415 | See |
Shared hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | False |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
{
"system_info": {
"python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1",
"setuptools": "49.6.0.post20210108",
"sklearn": "0.24.1",
"numpy": "1.20.2",
"scipy": "1.6.2",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": null,
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
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"prefix": "libopenblas",
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"version": "0.3.12",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
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"version": null,
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}
],
"cpu_count": 2
}